There Are Twins In The Future Of Predictive Maintenance

GE, Siemens and Lufthansa Technik are some of the companies focusing on new technique for boosting reliability.

A recent IFS study of top commercial aviation pain points found operational availability to be the greatest challenge, cited by 60% of respondents. “No matter how quickly operators can turn around a plane between flights, the disruption of having an aircraft on the ground has drastic ramifications, with parts and time potentially costing millions of dollars,” notes Mark Martin, IFS director of aerospace defense.

The solution lies in analyzing reliability data, and IFS has predicted that digital twins will play a bigger role in this task for commercial aviation. “In 2019, we will see MROs take advantage of digital twin information gained from original equipment manufacturers to capture much of this valuable reliability data,” Martin predicts.

The trend has already started. Martin says GE has built digital twin components for some of it engines. Furthermore, “GE also helped develop the world’s first digital twin for an airplane’s landing gear. Sensors placed on typical failure points on the asset, such as hydraulic pressure and brake temperature, provide real-time data and help predict early malfunctions or diagnose the remaining lifecycle of the landing gear.”

Another example of digital twinning is Siemens, which has implemented digital twin capabilities for components such as its electric propulsion units for manned and unmanned vehicles.

In commercial aviation, Lufthansa Technik also sees twinning as the future or performance monitoring and predictive maintenance to improve reliability. "Digital Twin refers to a digital replica of physical assets, processes and systems,” explains Alexander Simon-Sichart, LHT’s head of technology innovation and research. “A digital twin continuously learns and updates itself from various sources to represent the current condition of the real-life counterpart. Information sources for the digital twin consist of data from the real-life counterpart, knowledge from human experts, data from similar or previous parts, and so forth.

The technique can be expensive, so will probably be applied first to major systems and components. But as experience is gained, digital twins could cover a wider range of aircraft components.

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